aiqbench analysis
This research addresses the inference speed bottleneck in byte-level hierarchical language models by introducing multi-byte prediction, which generates multiple bytes in parallel without adding parameters or degrading performance. By aligning prediction windows with latent tokens and using a novel attention-masking scheme to maintain causality, the method achieves an optimal trade-off between throughput and accuracy across various generative tasks. This advancement is significant for optimizing large-scale language model deployment where inference latency is critical.
Dynamic Multi-Byte Prediction With Hierarchical Language Models
arXiv:2608.15454v1 Announce Type: new Abstract: Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization. However, generating one byte at a time remains a bottleneck for inference speed. To address this, we introduce multi-byte prediction (MBP), which generates multiple bytes in parallel, speeding up inference with minimal performance impact and no additional parameters. MBP builds on the popular multi-token prediction (MTP) paradigm with two crucial innovations. First, we introduce a variable-length prediction window that aligns with the latent tokens, or segments, of a hierarchical LM. Second, we implement a novel attention-masking scheme that enables parallel byte prediction without violating causality. We show that multi-byte prediction strikes a Pareto-optimal trade-off across multiple generative tasks, instruction following, question answering, summarization, and machine translation, achieving the best trade-off between performance and inference throughput.
Abstract:Byte-level hierarchical language models (LMs) have recently emerged as a robust alternative to their popular counterparts that use subword tokenization. However, generating one byte at a time remains a bottleneck for inference speed. To address this, we introduce multi-byte prediction (MBP), which generates multiple bytes in parallel, speeding up inference with minimal performance impact and no additional parameters. MBP builds on the popular multi-token prediction (MTP) paradigm with two crucial innovations. First, we introduce a variable-length prediction window that aligns with the latent tokens, or segments, of a hierarchical LM. Second, we implement a novel attention-masking scheme that enables parallel byte prediction without violating causality. We show that multi-byte prediction strikes a Pareto-optimal trade-off across multiple generative tasks, instruction following, question answering, summarization, and machine translation, achieving the best trade-off between performance and inference throughput.
Artificial Intelligence (cs.AI)
arXiv:2608.15454 [cs.AI]
(or arXiv:2608.15454v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2608.15454 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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